With rapid development of Internet technologies, network image data increases rapidly at an amazing speed. When wanting to use such massive data resources, a common Internet user needs to retrieve images. When an image is retrieved by using a keyword, generally many images that are somehow associated with the keyword are obtained, but many images that have little or no association with a result needed by a user may also be obtained simultaneously.
In recent years, search engine operators represented by Google, Bing, and Baidu all offer an image search function, which provides a service for a common user to retrieve massive network image data. At present, there are two image retrieval manners: image retrieval using a keyword, and content based image retrieval (CBIR). The image retrieval using a keyword is the most popular manner at present, and can use image tag information that is based on a user input to perform accurate image semantic matching. However, a search result usually includes many images that do not satisfy a demand of a user, which is caused by reasons such as an inaccurate or unprofessional word used in searching by a common user and much content contained in text of an article that matches an image. The CBIR is a research focus in the field of computer vision and information retrieval in recent years, and a research objective is to perform effective retrieval by using visual information of an image itself (searching an image by using an image). However, due to diversity of visual information of the image itself and existence of “semantic gap”, a retrieval effect of CBIR still does not meet a requirement of actual use.
In conclusion, a current image retrieval manner cannot bring a satisfactory effect to a user, and it becomes an important demand that obtained images are re-organized and re-ranked according to specific information provided by the user, so that the user can spend fewer efforts viewing more images that satisfy a requirement.